With traditional methods, the dimensionality of the parameter space is crucial for
numerical optimizers and gradient approximation.
In addition, second-order optimizers like L-BFGS [1]}, [2]}, [3]}, [4]}, [5]}
need to store an
approximation of the function curvature. Our proposed method provides exact
gradients without costly numerical gradient estimation and uses first-order
optimization methods – Stochastic Gradient Descent [6]} –
that are suitable for the stochastic setting.
This is faster and
more stable than numeric differentiation and more flexible than hand-coded
derivatives of particular potentials.
